HDG-select: A novel GUI based application for gene selection and classification in high dimensional datasets

Hameed, Shilan S.; Hassan, Rohayanti; Hassan, Wan Haslina; Muhammadsharif, Fahmi F.; Latiff, Liza Abdul

doi:10.1371/journal.pone.0246039

Cited by 6 publications

(4 citation statements)

References 57 publications

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“…We compared the performances of our proposed filter based methods ( MGS f and MGS rf ) to other already renowned methods such as RF (filter) [ 40 ], fDNN (embedded) [ 4 ], IGIS + (hybrid) [ 20 ], HDG (hybrid) [ 26 ] and mDSM (filter) [ 11 ].…”

Section: Resultsmentioning

confidence: 99%

“…In this method, informative genes are selected in autism spectrum disorder by utilizing a combination of various statistical filters and a wrapper-based Geometric Binary Particle Swarm Optimization-Support Vector Machine (GBPSO-SVM) algorithm. Another recent hybrid method was introduced by Hameed et al [ 26 ] named HDG-select. It provides a graphical user interface that uses mixed filter-GBPSO-SVM for feature selection, while SVM is used for disease classification.…”

Section: Introductionmentioning

confidence: 99%

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Use of relevancy and complementary information for discriminatory gene selection from high-dimensional gene expression data

et al. 2021

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With the advent of high-throughput technologies, life sciences are generating a huge amount of varied biomolecular data. Global gene expression profiles provide a snapshot of all the genes that are transcribed in a cell or in a tissue under a particular condition. The high-dimensionality of such gene expression data (i.e., very large number of features/genes analyzed with relatively much less number of samples) makes it difficult to identify the key genes (biomarkers) that are truly attributing to a particular phenotype or condition, (such as cancer), de novo. For identifying the key genes from gene expression data, among the existing literature, mutual information (MI) is one of the most successful criteria. However, the correction of MI for finite sample is not taken into account in this regard. It is also important to incorporate dynamic discretization of genes for more relevant gene selection, although this is not considered in the available methods. Besides, it is usually suggested in current studies to remove redundant genes which is particularly inappropriate for biological data, as a group of genes may connect to each other for downstreaming proteins. Thus, despite being redundant, it is needed to add the genes which provide additional useful information for the disease. Addressing these issues, we proposed Mutual information based Gene Selection method (MGS) for selecting informative genes. Moreover, to rank these selected genes, we extended MGS and propose two ranking methods on the selected genes, such as MGSf—based on frequency and MGSrf—based on Random Forest. The proposed method not only obtained better classification rates on gene expression datasets derived from different gene expression studies compared to recently reported methods but also detected the key genes relevant to pathways with a causal relationship to the disease, which indicate that it will also able to find the responsible genes for an unknown disease data.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of relevancy and complementary information for discriminatory gene selection from high-dimensional gene expression data

et al. 2021

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“…Project and shared in figshare database (https://figshare. com/articles/dataset/The_microarray_dataset_of_colon_ cancer_in_csv_format_/13658790/1) was used (8). The dataset includes expression levels of 2000 genes from 62 different samples (22 healthy and 40 tumor tissues).…”

Section: In This Study An Open Source Colon Cancer Gene Expression Da...mentioning

confidence: 99%

Artificial Intelligence-based Colon Cancer Prediction by Identifying Genomic Biomarkers

PAKSOY

Yağın

2022

Medical Records

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Colon cancer is the third most common type of cancer worldwide. Because of the poor prognosis and unclear preoperative staging, genetic biomarkers have become more important in the diagnosis and treatment of the disease. In this study, we aimed to determine the biomarker candidate genes for colon cancer and to develop a model that can predict colon cancer based on these genes. Material and Methods: In the study, a dataset containing the expression levels of 2000 genes from 62 different samples (22 healthy and 40 tumor tissues) obtained by the Princeton University Gene Expression Project and shared in the figshare database was used. Data were summarized as mean ± standard deviation. Independent Samples T-Test was used for statistical analysis. The SMOTE method was applied before the feature selection to eliminate the class imbalance problem in the dataset. The 13 most important genes that may be associated with colon cancer were selected with the LASSO feature selection method. Random Forest (RF), Decision Tree (DT), and Gaussian Naive Bayes methods were used in the modeling phase. Results: All 13 genes selected by LASSO had a statistically significant difference between normal and tumor samples. In the model created with RF, all the accuracy, specificity, f1-score, sensitivity, negative and positive predictive values were calculated as 1. The RF method offered the highest performance when compared to DT and Gaussian Naive Bayes. Conclusion:In the study, we identified the genomic biomarkers of colon cancer and classified the disease with a high-performance model. According to our results, it can be recommended to use the LASSO+RF approach when modeling high-dimensional microarray data.

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“…Hameed, S. S., Hassan, R(2021) [17] An efficient Binary Biogeography Based Optimization (BBO) based optimizer with Recursive Feature Elimination for Feature Selection (SVM-RFE) method was proposed. BBO-SVM-RFE used the SVM-RFE to improve the quality of the habitats by selecting the most relevant features in each habitat, which are the most relevant features for the classification task and omitting the irrelevant ones.…”

Section: Hdg-selectmentioning

confidence: 99%

An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data

Elkafrawy

Fathi²,

Qaraad

et al. 2021

IEEE Access

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Feature selection is critical in analyzing microarray data, which has many features (genes) or dimensions. However, with only a few samples, the large search space and time consumed during their selection make selecting relevant and informative genes that improve classification performance a complex task. This paper proposed a hybrid model for gene selection known as (SVM-mRMRe). The proposed model provides a framework for combining filter-based, ensemble, and embedded methods to select the most relevant and informative genes from high-dimensional microarray data by fusing embedded SVM coefficients (features ranking) with ensemble mRMRe. Eight of the most commonly used microarray datasets for various types of cancer were used to evaluate the model. The selected subset feature is evaluated by four different types of classifiers: random forest (RF), multilayer perceptron (MLP), k-nearest neighbors (k-NN), and Support Vector Machine (SVM). The experimental results show that the proposed model reduces time consumption and dimensionality and improves the differentiation of cancer tissues from benign tissues. Furthermore, the selected genes for the brain cancer dataset are biologically interpreted, and it agrees with the findings of relevant biomedical studies and plays an important role in patient prognosis.

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HDG-select: A novel GUI based application for gene selection and classification in high dimensional datasets

Cited by 6 publications

References 57 publications

Use of relevancy and complementary information for discriminatory gene selection from high-dimensional gene expression data

Use of relevancy and complementary information for discriminatory gene selection from high-dimensional gene expression data

Artificial Intelligence-based Colon Cancer Prediction by Identifying Genomic Biomarkers

An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data

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